NEWS

multiflexscan 0.3.0

Added a self-contained workflow vignette and runnable examples for the result accessors and model-diagnostic methods.
Added model_criteria() to extract all model-selection criteria by cluster count, with selected = TRUE to return only the selected model.
Added C_criterion() and RDC() for the selected model's criteria.
Added a logLik() method returning the selected Poisson model's likelihood, effective parameter count, and number of observations without refitting.
AIC() now respects k; AIC() and BIC() support multiple fitted objects through the standard R methods. Previously additional arguments were ignored.
These extraction methods also work with complete saved 0.2.0 objects. The cluster search, model selection, and Monte Carlo algorithms are unchanged.

Improved reproducibility of Monte Carlo results under parallel computation by using doRNG (%dorng%), which provides independent random streams across workers. A new seed argument seeds sequential runs via set.seed() and parallel runs via registerDoRNG(). Using set.seed() alone is not sufficient when cores > 1.
Added accessors nclusters(), pvalue(), clusters(), and get_setting().
Added S3 methods as.data.frame(), coef(), nobs(), AIC(), and BIC() for multiflexscan objects so that results can be extracted without relying on the internal list structure.

When nb is supplied as a list, areas whose neighbor indices fall outside [1, nrow(coordinates)] are skipped when building the adjacency matrix, instead of stopping with an error. Such areas are treated as adjacent to no other areas.
Null Monte Carlo replications now use the same simulated counts for both candidate search (runFleXScan) and the subsequent GLM / RDC steps. Previously the GLM always used the observed counts.
Empty candidate sets no longer error in the cluster p-value loop (seq_along instead of 1:length). Null replications with no candidates contribute -Inf as the maximum scan statistic (instead of NA).
When several values of K attain the same maximum RDC, nclust is the smallest such K (which.max), yielding a single integer.

Added multiflexscan() for detecting multiple spatial disease clusters using the information criterion and scan statistic approach of Takahashi and Shimadzu (2020), built on rflexscan::runFleXScan().
Supports flexible and circular scan statistics, original and restricted likelihood ratio statistics, and hot-, cold-, and both-type cluster scanning.
Returns candidate clusters, relative difference criterion (RDC) values, selected number of clusters, and Monte Carlo p-values for the overall test and individual clusters.
Added S3 methods print(), summary(), and plot() for multiflexscan objects.
Added choropleth() to map selected clusters on an sf object.
Parallel Monte Carlo replications via foreach and doSNOW.